LEVERAGING EXPLAINABLE AI TO IMPROVE BREAST CANCER DETECTION RATE USING TRANSFER LEARNING WITH DEEP RECURRENT CONVOLUTIONAL NEURAL NETWORKS

Breast cancer remains a significant global health concern, emphasizing the need for accurate and timely diagnosis. Leveraging advancements in artificial intelligence (AI), particularly deep learning techniques, has shown promise in improving breast cancer detection rates. In this study, we propose a novel approach that integrates explainable AI principles with transfer learning using deep recurrent convolutional neural networks (RCNNs) to enhance breast cancer detection. The proposed model combines the spatial feature extraction capabilities of convolutional layers with the sequential processing capabilities of recurrent layers, thereby capturing both local patterns and temporal dependencies in mammogram images. Additionally, the incorporation of explainable AI techniques facilitates interpretation and understanding of the model's decisions, enhancing its clinical utility. We evaluate the performance of the proposed approach on a publicly available mammography dataset and demonstrate its effectiveness in improving breast cancer detection rates compared to baseline models. Furthermore, we provide insights into the learned representations and decision-making processes of the model, thereby enhancing transparency and trust in AI-assisted diagnosis. Our findings underscore the potential of explainable AI-driven transfer learning with deep RCNNs as a valuable tool for augmenting radiologists' capabilities and improving patient outcomes in breast cancer screening and diagnosis.